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Akhil-Theerthala
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https://akhiltheerthala.com

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reacted to tsungyi's post with 🔥 about 1 hour ago
🎉 Exciting News — NVIDIA Cosmos is celebrating its 1st birthday and has hit 5 MILLION downloads! 🎉 In just one year, the Cosmos ecosystem has grown rapidly: 🧠 Cosmos Reason and Cosmos Predict have surpassed 2 MILLION downloads each on @HuggingFace, topping physical AI leaderboards 🔄 Cosmos Transfer is enabling adaptation across domains and tasks 🔮 Cosmos Cookbook is the go-to hub for recipes from developers and partners like Uber and IntBot. Thank you to our amazing developer community for making this possible. Here's to pushing the boundaries of world foundation models together! 🧑🏻‍🍳Read the Cosmos Cookbook: https://nvda.ws/4qevli8 📚 Explore Models & Datasets: https://huggingface.co/collections/nvidia/nvidia-cosmos-2
reacted to their post with 👍 about 1 hour ago
Is it better to show a model too many images once (Diversity), or extract as much information from a small set of images? I have always wanted to do an ablation study on this and recently I got the chance to do exactly that. Why? In applied domains like robotics, manufacturing, or banking, we rarely have the luxury of internet-scale diverse image datasets. We are often "Data Poor" in terms of diversity but "Data Rich" in depth. The takeaway? Density is efficient for facts but dangerous for reasoning (logical collapse) if you don't have larger scale data. More details: https://huggingface.co/blog/Akhil-Theerthala/diversity-density-for-vision-language-models
commented on their article about 11 hours ago
Diversity Vs Density: A data strategy comparison for fine-tuning VLMs
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